Problem Description

The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. The classification goal is to predict if the client will subscribe a term deposit (variable y).

Data Description

Input variables:

Bank Client data:

age (numeric) job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')

marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)

education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')

default: has credit in default? (categorical: 'no','yes','unknown')

housing: has housing loan? (categorical: 'no','yes','unknown')

loan: has personal loan? (categorical: 'no','yes','unknown')

Related with the last contact of the current campaign:

contact: contact communication type (categorical: 'cellular','telephone')

month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')

day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')

duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

Other attributes:

campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)

pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)

previous: number of contacts performed before this campaign and for this client (numeric)

poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

Social and economic context attributes

emp.var.rate: employment variation rate - quarterly indicator (numeric)

cons.price.idx: consumer price index - monthly indicator (numeric)

cons.conf.idx: consumer confidence index - monthly indicator (numeric)

euribor3m: euribor 3 month rate - daily indicator (numeric)

nr.employed: number of employees - quarterly indicator (numeric)

Output variable (desired target):

y - has the client subscribed a term deposit? (binary: 'yes','no')

There is no null value present in the given dataset

Exploratory Data Analysis

Univariate Analysis

Bivariate Analysis

Encoding the Categorical Variables

Extra Trees Classifier

Logistic Regression

Since a good classifier stays as far away from that line as possible we can assume that our model works fine

DecisionTree Classifier

The DecisionTree Classifier model is optimum because train and test accuracy both are 82% approximately.

RandomForest Classifier

RandomForest Classifier(Hyperparameter Tuned)

XGBRF Classifier

XGBoost Classifier

Model Summary

SHAP Implementation

Local interpretibility